Exam Prep – Databricks Certified Generative AI Engineer Associate
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Learning Track
Exam Prep
Delivery methods
On-Site, Virtual
Duration
1 day
This course uses a question-first methodology to prepare learners for the Databricks Generative AI Engineer Associate exam. Each module presents realistic, exam-style practice questions aligned with official domains. Instructors guide learners through every answer choice—highlighting why each is correct or incorrect—while weaving in focused explanations to reinforce understanding of Databricks tools and generative AI workflows.
Learning Objectives
By the end of this course, learners will be able to:
- Analyze and answer Databricks Generative AI Engineer Associate exam questions with clarity.
- Understand the rationale behind both correct and incorrect choices.
- Apply concepts like prompt engineering, RAG pipelines, model governance, and LLM monitoring using Databricks-native tools (Vector Search, MLflow, Model Serving, Unity Catalog).
- Bolster exam-day confidence by developing a test-taking mindset and familiarity with question patterns.
Audience
- Aspiring Databricks Certified Generative AI Engineer Associate candidates
- AI engineers, ML practitioners, and data professionals with ~6+ months of experience in Databricks or generative AI environments
- Learners who prefer an exam-oriented, practice-driven training approach
Prerequisites
- Working knowledge of Python (especially for AI workflows and orchestration)
- Practical experience with Databricks tools: LLM chains, vector indexing, model serving, MLflow, prompt strategies, and governance frameworks
- General familiarity with prompt engineering, RAG (Retrieval-Augmented Generation) pipelines, and AI model deployment
Course outline
- Certification format, domains, scoring, timing
- Strategies for analyzing multiple-choice questions
- Question types and common distractors
- Pacing strategies and flagging questions for review
- Prompt engineering: Crafting structured output, aligning business goals with model behavior
- Problem decomposition into LLM chains and RAG components
- Choosing appropriate chain tools and mapping desired pipeline inputs and outputs
- Sequencing tools and chain steps for multi-stage reasoning
- Implementing chunking strategies tailored to document structures and model context windows
- Filtering out low-quality or irrelevant content to enhance RAG retrieval
- Extracting source data (e.g., PDFs, text) using suitable Python libraries
- Writing chunked data into Delta Lake tables under Unity Catalog governance
- Identifying high-value documents for retrieval
- Measuring and optimizing retrieval performance using metrics and evaluation tools
- Selecting tools like LangChain (or equivalents) for LLM-based application workflows
- Understanding how prompt format shifts affect model outputs
- Evaluating responses for issues such as hallucination, bias, or quality lapses
- Applying chunking strategies based on models and retrieval results
- Dynamically augmenting prompts with context based on user intent
- Implementing guardrails (e.g., safe token filtering, system prompts)
- Writing meta-prompts to reduce hallucination and data leakage
- Building agent-based prompt templates with available functions
- Choosing models or embedding strategies based on metadata and experimental metrics
- Architecting complete RAG pipelines in Databricks
- Registering models and creating Vector Search indexes
- Deploying models as pyfunc endpoints using Model Serving
- Configuring endpoint parameters and orchestration workflows for production use
- Designing safe guardrail policies and mitigation strategies
- Addressing legal and licensing considerations in generative AI use
- Applying data masking and privacy-preserving techniques
- Defining and tracking LLM performance metrics, response quality, and usage logging
- Utilizing MLflow for experiment tracking and lifecycle management
- Implementing operational monitoring, cost tracking, and alerting for model endpoints
